Seasonal lead release to drinking water and the effect of aluminum
Bibliographic record
Abstract
Monitoring lead in drinking water is important for public health, but seasonality in lead concentrations can bias monitoring programs if it is not understood and accounted for. Here, we describe an apparent seasonal pattern in lead release to orthophosphate-treated drinking water, identified through point-of-use sampling at sites in Halifax, Canada, with various sources of lead. Using a generalized additive model, we extracted the seasonally-varying components of time series representing a suite of water quality parameters and we identified aluminum as a correlate of lead. To investigate aluminum’s role in lead release, we modeled the effect of variscite (AlPO4 · 2H2O) precipitation on lead solubility, and we evaluated the effects of aluminum, temperature, and orthophosphate concentration on lead release from new lead coupons. At environmentally relevant aluminum and orthophosphate concentrations, variscite precipitation increased predicted lead solubility by decreasing available orthophosphate. Increasing the aluminum concentration from 20–500 µg L-1 increased lead release from coupons by 41% and modified the effect of orthophosphate, rendering it less effective. We attributed this to a decrease in the concentration of soluble (<0.45 µm) phosphorus with increasing aluminum and an accompanying increase in particulate lead and phosphorus (>0.45 µm).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".